Probability and Statistics [GTU]
₹500.00

Basis Probability
 Introduction to Probability Distribution
 Discrete Random Variable in Probability Distribution
 Distribution function of Discrete Random Variable
 Continuous Random Variable in Probability Distribution
 Continuous Distribution Function in Probability Distribution
 Expectation in Probability Distribution
 Mean and Variance Part 1 in Probability Distribution
 Mean and Variance Part 2 in Probability Distribution
 Moments and Moments Generating Function Part #1 in Probability Distribution
 Moments and Moments Generating Function Part #2 in Probability Distribution

Some Special Probability Distribution

Basic Statistics

Applied Statistics
 What Is Sampling
 Large Sampling Problems
 Large Sampling Test Theory+Numerical
 Hypothesis Testing Full concept
 Testing Difference Between Means(case_1) With Examples
 Small Sample Test Example
 Small Sample Test
 Testing Difference Between Means(case_1) With Examples Part 2
 Testing Difference Between Means(case_2)
 Non Parametric Test Type_1_Numerical
 Non Parametric Test

Curve Fitting
Probability and Statistics
Prerequisite of studying this subject is just Probability basics concepts.
Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speaking, 0 indicates impossibility of the event and 1 indicates certainty. The higher the probability of an event, the more likely it is that the event will occur. A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the two outcomes (“heads” and “tails”) are both equally probable; the probability of “heads” equals the probability of “tails”; and since no other outcomes are possible, the probability of either “heads” or “tails” is 1/2 (which could also be written as 0.5 or 50%). These concepts have been given an axiomatic mathematical formalization in probability theory, which is used widely in areas of study such as statistics, mathematics, science, finance, gambling, artificial intelligence, machine learning, computer science, game theory, and philosophy to, for example, draw inferences about the expected frequency of events. Probability theory is also used to describe the underlying mechanics and regularities of complex systems. The word probability derives from the Latin probabilitas, which can also mean “probity”, a measure of the authority of a witness in a legal case in Europe, and often correlated with the witness’s nobility. In a sense, this differs much from the modern meaning of probability, which in contrast is a measure of the weight of empirical evidence, and is arrived at from inductive reasoning and statistical inference.
Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as “all people living in a country” or “every atom composing a crystal”. Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments. When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation.
Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution’s central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena.
Chapter Basic Probability consists of the following subtopics Experiment, definition of probability, conditional probability, independent events, Bayes’ rule, Bernoulli trials, Random variables, discrete random variable, probability mass function, continuous random variable, probability density function, cumulative distribution function, properties of cumulative distribution function, Two dimensional random variables and their distribution functions, Marginal probability function, Independent random variables. Chapter Some special Probability Distributions consists of the following subtopics Binomial distribution, Poisson distribution, Poisson approximation to the binomial distribution, Normal, Exponential and Gamma densities, Evaluation of statistical parameters for these distributions. Chapter Basic Statistics consists of the following subtopics Measure of central tendency: Moments, Expectation, dispersion, skewness, kurtosis, expected value of two dimensional random variable, Linear Correlation, correlation coefficient, rank correlation coefficient, Regression, Bounds on probability, Chebyshev‘s Inequality. Chapter Applied Statistics consists of the following subtopics Formation of Hypothesis, Test of significance: Large sample test for single proportion, Difference of proportions, Single mean, Difference of means, and Difference of standard deviations. Chapter Test of significance for Small samples consists of the following subtopics t Test for single mean, difference of means, ttest for correlation coefficients, F test for ratio of variances, Chisquare test for goodness of fit and independence of attributes. Chapter Curve fitting by the numerical method consists of the following subtopics Curve fitting by of method of least squares, fitting of straight lines, second degree parabola and more general curves.
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Course Features
 Lectures 35
 Quizzes 0
 Duration 50 hours
 Skill level All levels
 Language English
 Students 2
 Certificate No
 Assessments Yes